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Deforestation, economic growth and corruption: a nonparametric analysis on the case of Amazon forest

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  • Cassandro Maria Mendes
  • Sabino Porto Junior

Abstract

This article aims to analyse the relationship between deforestation, corruption and economic growth. We focused our analysis on the Brazilian municipalities situated in the Amazon region. We used the cross-sectional data from 538 municipalities in the Legal Amazon area for the year 2004. We constructed the municipal corruption index based on the objective data delivered by the reports from Controladoria-Geral da União (CGU). We performed a nonparametric analysis, that is, we applied the Generalized Additive Model (GAM). We found statistically significant effects of the economic growth rate on the level of deforestation. However, the corruption seems to have no significant impact. Given the first results, we divided our data and analysed just the data from 25 municipalities from the states Pará and Matogrosso. In the second approach, corruption was statistically significant.

Suggested Citation

  • Cassandro Maria Mendes & Sabino Porto Junior, 2012. "Deforestation, economic growth and corruption: a nonparametric analysis on the case of Amazon forest," Applied Economics Letters, Taylor & Francis Journals, vol. 19(13), pages 1285-1291, September.
    • Handle: RePEc:taf:apeclt:v:19:y:2012:i:13:p:1285-1291
    DOI: 10.1080/13504851.2011.619487
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    Cited by:

    1. Mouna Ben Abdeljelil & Christophe Rault & Fateh Belaïd, 2023. "Economic growth and pollutant emissions: new panel evidence from the union for the Mediterranean countries," Economic Change and Restructuring, Springer, vol. 56(3), pages 1537-1566, June.
    2. Wilfredo L. Maldonado & Jessica A. Barbosa, 2023. "Determinants of Agricultural Fires: An Aggregative Games Approach," Working Papers, Department of Economics 2023_12, University of São Paulo (FEA-USP).
    3. Sencer Atasoy, Burak, 2017. "Testing the environmental Kuznets curve hypothesis across the U.S.: Evidence from panel mean group estimators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 731-747.

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